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1. WO2020097562 - MACHINE LEARNING-BASED PREDICTION, PLANNING, AND OPTIMIZATION OF TRIP TIME, TRIP COST, AND/OR POLLUTANT EMISSION DURING NAVIGATION

Publication Number WO/2020/097562
Publication Date 14.05.2020
International Application No. PCT/US2019/060618
International Filing Date 08.11.2019
IPC
B61L 3/00 2006.01
BPERFORMING OPERATIONS; TRANSPORTING
61RAILWAYS
LGUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
3Devices along the route for controlling devices on the vehicle or vehicle train, e.g. to release brake, to operate a warning signal
G08G 1/00 2006.01
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
G08G 1/0967 2006.01
GPHYSICS
08SIGNALLING
GTRAFFIC CONTROL SYSTEMS
1Traffic control systems for road vehicles
09Arrangements for giving variable traffic instructions
0962having an indicator mounted inside the vehicle, e.g. giving voice messages
0967Systems involving transmission of highway information, e.g. weather, speed limits
CPC
G01C 21/20
GPHYSICS
01MEASURING; TESTING
CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
21Navigation; Navigational instruments not provided for in preceding groups G01C1/00-G01C19/00
20Instruments for performing navigational calculations
G06F 2111/10
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
2111Details relating to CAD techniques
10Numerical modelling
G06F 30/20
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
30Computer-aided design [CAD]
20Design optimisation, verification or simulation
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06N 5/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
04Inference methods or devices
Applicants
  • IOCURRENTS, INC. [US]/[US]
Inventors
  • BHATTACHARYYA, Bhaskar
  • KING, Cosmo
  • FRIEDMAN, Samuel
  • HENDERSON, Kiersten
  • RUST, Alexa
Agents
  • LI, Yiheng
  • OBEIDAT, Baha, A.
  • KUMABE, Blake, K.
  • SOLTANI, Bobby, B.
  • ZENTZ, Bradley, J.
  • SARGEANT, Brooke
  • QUIST, Brooke, W.
  • ROTH, Carol, J.
  • EIDT, Chandra, E.
  • WITTKOPP, Cristina, J.
  • O'BRIEN, Daniel
  • CARLSON, David, V.
  • STARK, Duncan
  • TARLETON, E., Russell
  • SUN, Eileen, S.
  • HARWOOD, Eric, A.
  • GERTZ, Glenda, A.
  • HAN, Hai
  • TALBERT, Hayley, J.
  • BARRETT, Jared, M.
  • SAKOI, Jeffrey, M.
  • BAUNACH, Jeremiah, J.
  • ZHANG, Jianping
  • MORGAN, John, A.
  • WAKELEY, John, J.
  • COE, Justin, E.
  • HENCKEL, Karen, M.
  • HEFTER, Karl, A.
  • HERMANNS, Karl, R.
  • MORGAN, Kevan, L.
  • COSTANZA, Kevin, S.
  • COOPER, Michael, P.
  • RUSYN, Paul
  • LIN, Qing
  • HALLER, Rachel, A.
  • IANNUCCI, Robert
  • WEBB, Samuel, E.
  • LEEK, Shoko, I.
  • ROSENMAN, Stephen, J.
  • ABEDI, Syed
  • BOLLER, Timothy, L.
  • LIGON, Toby, J.
  • SHEWMAKE, Thomas, A.
  • CANTRELL, Tyler, C.
  • MIXCO, Javier, M.
  • LIU, Shi
  • LAWRENZ, Steven, D.
  • CLAYTON, Kenneth, W.
  • SAKOI, Zachary, M.
Priority Data
62/758,38509.11.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) MACHINE LEARNING-BASED PREDICTION, PLANNING, AND OPTIMIZATION OF TRIP TIME, TRIP COST, AND/OR POLLUTANT EMISSION DURING NAVIGATION
(FR) PRÉDICTION, PLANIFICATION ET OPTIMISATION BASÉES SUR L'APPRENTISSAGE AUTOMATIQUE DU TEMPS DE VOYAGE, DU COÛT DE VOYAGE ET/OU DE L'ÉMISSION DE POLLUANTS PENDANT LA NAVIGATION
Abstract
(EN)
A method of predicting, in real-time, a relationship between a vehicle's engine speed, trip time, cost, and fuel consumption, comprising: monitoring vehicle operation over time to acquiring data representing at least a vehicle location, a fuel consumption rate, and operating conditions; generating a predictive model relating the vehicle's engine speed, trip time, and fuel consumption; and receiving at least one constraint on the vehicle's engine speed, trip time, and fuel consumption, and automatically producing from at least one automated processor, based on the predictive model, a constrained output.
(FR)
L'invention concerne un procédé de prédiction, en temps réel, d'une relation entre la vitesse du moteur d'un véhicule, le temps de voyage, le coût et la consommation de carburant, consistant à : surveiller un fonctionnement de véhicule dans le temps pour acquérir des données représentant au moins un emplacement de véhicule, un taux de consommation de carburant et des conditions de fonctionnement ; générer un modèle prédictif relatif à la vitesse du moteur du véhicule, au temps de voyage et à la consommation de carburant ; et recevoir au moins une contrainte sur la vitesse du moteur du véhicule, le temps de voyage et la consommation de carburant, et produire automatiquement à partir d'au moins un processeur automatisé, sur la base du modèle prédictif, une sortie contrainte.
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